A Modelling Framework for Evidence-Based Public Health Policy Making

Prasinos, Marios; Basdekis, Ioannis; Anisetti, Marco; Spanoudakis, George; Koutsouris, Dimitris; Damiani, Ernesto

doi:10.1109/jbhi.2022.3142503

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…The 7 tools reviewed are summarized in Table 2. Three integrated platforms (EVOTION, MIDAS, CrowdHEALTH) were designed to support public health policy decisions for a range of conditions and include a data analytics component supporting both descriptive and predictive analytics [22][23][24][25][26]. Users can create policy models, define the way in which data should be analyzed in order to produce the evidence useful for public health policymaking and obtain analytical results of how this evidence may support or contradict various policy actions.…”

Section: Tools Overviewmentioning

confidence: 99%

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Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review (Preprint)

Dritsakis,

Gallos,

Psomiadi

et al. 2024

Preprint

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BACKGROUND There is an emerging need for evidence-based approaches harnessing large amounts of healthcare data and novel technologies (such as Artificial Intelligence, AI) to optimize public health policy-making. OBJECTIVE The aim of the present study was to explore the data analytics tools designed specifically for policy-making in Non-Communicable Diseases (NCDs) and their implementation. METHODS A scoping review was conducted after searching the PubMed database for articles published in the last 10 years. RESULTS Nine articles that presented 7 data analytics tools designed to inform policy-making for NCDs were reviewed. Tools incorporate descriptive and predictive analytics. Some tools were designed to include recommendations for decision support but no pilot studies have been published that apply prescriptive analytics. The tools were piloted with a range of conditions with cancer being the condition least studied. Implementation of tools included use cases, pilots or evaluation workshops were reported that involved policy-makers. However, our findings demonstrate very limited real-world use of analytics by policy-makers, in line with previous studies. CONCLUSIONS Despite the availability of tools designed for different purposes and conditions, data analytics are not widely used to support policy-making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy-making. Based on the findings we make a number of suggestions for researchers developing digital tools to support public health policy-making. The findings will also serve as input for the EU-funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform.

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Section: Tools Overviewmentioning

confidence: 99%

“…Three out of 7 tools were decision support systems designed to make policy recommendations. Authors referred to the prescriptive capabilities of the tools and demonstrated the policy creation process [20,22,25]. However, none of them presented a related use case actually applying this type of analytics.…”

Section: Prescriptive Analyticsmentioning

confidence: 99%

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review (Preprint)

Dritsakis,

Gallos,

Psomiadi

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Our reference scenario is the H2020 EVOTION Policy Making Big Data Platform [18,26], a collaborative solution offering analytics services or computing/data nodes based on different computation/storage frameworks. The platform, based on the Apache framework and Big Data Analytics-as-a-Service [25], offers an easy-to-use framework for policy makers to develop evidence-based policies following analytics results.…”

Section: Reference Scenariomentioning

confidence: 99%

“…Second, we define an assurance methodology where: i) clients annotate a pipeline template with assurance requirements modeling their trust expectations in terms of non-functional properties, ii) a pipeline instance is generated from the template mapping all tasks, services, and requirements on real components, iii) an assurance confidence level is calculated modeling the trustworthiness level of the Big Data pipeline. Third, we evaluated our approach in the context of the H2020 EVOTION Policy-Making Big Data Platform [18], where policy makers design or select analytics templates to be instantiated and executed by the platform in a fully assisted and privacy-preserving way.…”

Section: Introductionmentioning

confidence: 99%

An assurance process for Big Data trustworthiness

Anisetti

Ardagna

Berto

2023

Future Generation Computer Systems

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The application of artificial intelligence in health policy: a scoping review

Ramezani,

Takian,

Bakhtiari

et al. 2023

BMC Health Serv Res

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Background Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI’s application and capabilities in health policymaking. Methods We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords “artificial intelligence” and “policymaking.” We used Walt and Gilson’s policy triangle framework for charting the data. Results The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts. Conclusion Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI’s potential to foster the meaningful use of evidence-based policymaking.

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A Modelling Framework for Evidence-Based Public Health Policy Making

Cited by 6 publications

References 39 publications

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review (Preprint)

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review (Preprint)

An assurance process for Big Data trustworthiness

The application of artificial intelligence in health policy: a scoping review

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